A Classification Method for Workers’ Physical Risk

Tamantini, Christian; Rondoni, Cristiana; Cordella, Francesca; Guglielmelli, Eugenio; Zollo, Loredana

doi:10.3390/s23031575

Cited by 6 publications

(4 citation statements)

References 48 publications

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“…The performance of the model presented in this paper is dependent on the experimental conditions under which the datasets were acquired and the choice of features extracted from the raw data collected 52 . For the experimental conditions reported in this work, the feature set that emerged is the optimal one in terms of accuracy and computational burden.…”

Section: Discussionmentioning

confidence: 99%

Affective state estimation based on Russell’s model and physiological measurements

Cittadini

Tamantini

Luzio

et al. 2023

Sci Rep

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Affective states are psycho-physiological constructs connecting mental and physiological processes. They can be represented in terms of arousal and valence according to the Russel’s model and can be extracted from physiological changes in human body. However, a well-established optimal feature set and a classification method effective in terms of accuracy and estimation time are not present in the literature. This paper aims at defining a reliable and efficient approach for real-time affective state estimation. To obtain this, the optimal physiological feature set and the most effective machine learning algorithm, to cope with binary as well as multi-class classification problems, were identified. ReliefF feature selection algorithm was implemented to define a reduced optimal feature set. Supervised learning algorithms, such as K-Nearest Neighbors (KNN), cubic and gaussian Support Vector Machine, and Linear Discriminant Analysis, were implemented to compare their effectiveness in affective state estimation. The developed approach was tested on physiological signals acquired on 20 healthy volunteers during the administration of images, belonging to the International Affective Picture System, conceived for inducing different affective states. ReliefF algorithm reduced the number of physiological features from 23 to 13. The performances of machine learning algorithms were compared and the experimental results showed that both accuracy and estimation time benefited from the optimal feature set use. Furthermore, the KNN algorithm resulted to be the most suitable for affective state estimation. The results of the assessment of arousal and valence states on 20 participants indicate that KNN classifier, adopted with the 13 identified optimal features, is the most effective approach for real-time affective state estimation.

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Section: Discussionmentioning

confidence: 99%

Affective state estimation based on Russell’s model and physiological measurements

Cittadini

Tamantini

Luzio

et al. 2023

Sci Rep

Self Cite

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“…Tamantini et al extracted cardiorespiratory information and compared it as features in different machine learning models. The results show that machine learning algorithms are quite effective in risk monitoring [7].…”

Section: Introductionmentioning

confidence: 93%

Assessment of Mental Workload Level Based on PPG Signal Fusion Continuous Wavelet Transform and Cardiopulmonary Coupling Technology

Zhang,

Wang,

Zhuang

et al. 2024

Electronics

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Mental workload is an important predisposing factor for mental illnesses such as depression and is closely related to individual mental health. However, the suboptimal accuracy of utilizing photoplethysmography (PPG) exclusively for mental workload classification has constrained its application within pertinent professional domains. To this end, this paper proposes a signal processing method that combines continuous wavelet transform (CWT) and cardiopulmonary coupling mapping (CPC) to classify mental load via a convolutional neural network (ResAttNet). The method reflects changes in mental workload, as assessed by changes in the association between heart rate variability and respiration. In this paper, the strengths and weaknesses of this method are compared with other traditional psychological workload monitoring methods, such as heart rate variability (HRV), and its validation is performed on the publicly available dataset MAUS. The experiments show that the method is significantly better than previous machine learning methods based on heart rate variability correlation. Meanwhile, the accuracy of the method proposed in this paper reaches 80.5%, which is 6.2% higher than in previous studies. It is comparable to the result of 82.4% for the ECG-based mental workload monitoring system. Therefore, the method of combining CWT and CPC has considerable potential and provides new ideas for mental workload classification.

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“…Given the feature scores, only those with positive weights are selected, while those with weights less than or equal to zero are discarded. This process aids in identifying the most influential attributes for accurate ripeness classification [32], [33].…”

Section: A the Proposed Multi-spectral Device For In-situ Ripeness Ev...mentioning

confidence: 99%

A low-cost multispectral device for in-field fruit ripening assessment

Lauretti,

Tamantini,

Zompanti

et al. 2024

IEEE Sensors J.

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When assessing fruit ripeness, multispectral sensors offer a cheaper alternative to high-resolution spectroscopy. Additionally, they provide a more robust solution to environmental factors, in contrast to multispectral cameras. However, the low spectral resolution of multispectral devices proposed in the literature may not be sufficient to discriminate with acceptable accuracy a high number of different ripeness classes with machine learning techniques. Moreover, meaningful features to be given as input to the classification models are generally hand-crafted on the basis of the fruit to be evaluated, making the devices not versatile across other fruit categories. This paper aims at overcoming these limitations, by introducing a multispectral device for in-situ fruitripening evaluation that is i) affordable for a wide range of end-users, ii) robust to environmental factors, iii) capable of automatically finding the most meaningful features depending on the fruit categories to evaluate and iv) capable of discriminating a high number of fruit ripeness classes with high accuracy. The device integrates a broadband LED and a VIS-NIR multispectral sensor to gather information about the fruit' ripeness levels through predictive machine learning algorithms. The proposed device was trained on a dataset of 450 spectral measurements acquired from tomatoes at six different ripeness stages. The results demonstrated the high capability of the proposed approach to recognize the tomatoes' ripeness levels (average accuracy of 93.72%).

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A Classification Method for Workers’ Physical Risk

Cited by 6 publications

References 48 publications

Affective state estimation based on Russell’s model and physiological measurements

Affective state estimation based on Russell’s model and physiological measurements

Assessment of Mental Workload Level Based on PPG Signal Fusion Continuous Wavelet Transform and Cardiopulmonary Coupling Technology

A low-cost multispectral device for in-field fruit ripening assessment

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